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| import streamlit as st | |
| import numpy as np | |
| import sys | |
| import time | |
| sys.path.insert(0, '.') | |
| from data.data_loader import load_data, preprocess, split_and_scale | |
| from src.llm_advisor import get_burnout_advice, get_burnout_chat_response | |
| def load_models(): | |
| from xgboost import XGBClassifier | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| df = load_data() | |
| X, y, feature_cols = preprocess(df) | |
| X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42) | |
| X_val, _, y_val, _ = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) | |
| scaler = StandardScaler() | |
| X_train_s = scaler.fit_transform(X_train) | |
| X_val_s = scaler.transform(X_val) | |
| model = XGBClassifier( | |
| n_estimators=100, max_depth=4, learning_rate=0.05, | |
| subsample=0.7, colsample_bytree=0.7, min_child_weight=5, | |
| reg_alpha=0.1, reg_lambda=1.0, eval_metric='logloss', | |
| early_stopping_rounds=15, random_state=42, | |
| ) | |
| model.fit(X_train_s, y_train, eval_set=[(X_val_s, y_val)], verbose=False) | |
| return model, scaler, feature_cols | |
| def get_top_risk_factors(user_input, feature_cols, model): | |
| """Return top-3 risk factors using XGBoost feature importances.""" | |
| importances = model.feature_importances_ | |
| scores = {} | |
| for col, imp in zip(feature_cols, importances): | |
| if col not in user_input: | |
| continue | |
| val = user_input[col] | |
| # Recovery/wellness features: low value = higher risk contribution | |
| recovery_features = [ | |
| 'SLEEP_HOURS', 'WEEKLY_MEDITATION', 'TIME_FOR_PASSION', 'FLOW', | |
| 'ACHIEVEMENT', 'SOCIAL_NETWORK', 'CORE_CIRCLE', 'LIVE_VISION', | |
| 'RECOVERY_SCORE', 'SOCIAL_SUPPORT_SCORE', 'LIFESTYLE_SCORE', 'HEALTH_HABITS', | |
| ] | |
| if col in recovery_features: | |
| scores[col] = imp * (1 - val / 10) | |
| else: | |
| scores[col] = imp * (val / 10) | |
| top3 = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:3] | |
| return {k: f"score {user_input.get(k, '?')}/10" for k, _ in top3} | |
| def main(): | |
| st.set_page_config(page_title="Burnout Risk Tracker", page_icon="🔥", layout="wide") | |
| st.title("🔥 Burnout Risk Tracker") | |
| st.markdown( | |
| "*Answer questions about your **recovery and lifestyle habits** — " | |
| "the model predicts whether you're showing early burnout risk.*" | |
| ) | |
| xgb_model, scaler, feature_cols = load_models() | |
| if 'conversation_history' not in st.session_state: | |
| st.session_state.conversation_history = [] | |
| if 'risk_score' not in st.session_state: | |
| st.session_state.risk_score = None | |
| if 'advice' not in st.session_state: | |
| st.session_state.advice = None | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| st.subheader("Your Lifestyle & Recovery Habits") | |
| st.caption( | |
| "These questions cover your wellness behaviors — not your current stress level. " | |
| "The model infers burnout risk from how you're living, not how you're feeling right now." | |
| ) | |
| user_input = {} | |
| st.markdown("**Sleep & Recovery**") | |
| user_input['SLEEP_HOURS'] = st.slider("Sleep hours per night", 0, 10, 7) | |
| user_input['WEEKLY_MEDITATION'] = st.slider("Meditation sessions per week", 0, 10, 2) | |
| user_input['TIME_FOR_PASSION'] = st.slider("Time for hobbies / passions", 0, 10, 3) | |
| st.markdown("**Work & Productivity**") | |
| user_input['TODO_COMPLETED'] = st.slider("Daily tasks completed", 0, 10, 5) | |
| user_input['FLOW'] = st.slider("Flow state at work", 0, 10, 5) | |
| user_input['ACHIEVEMENT'] = st.slider("Sense of achievement", 0, 10, 5) | |
| user_input['LIVE_VISION'] = st.slider("Clarity of life vision", 0, 10, 5) | |
| st.markdown("**Social & Support**") | |
| user_input['SOCIAL_NETWORK'] = st.slider("Strength of social network", 0, 10, 5) | |
| user_input['CORE_CIRCLE'] = st.slider("Close / trusted relationships", 0, 10, 5) | |
| user_input['SUPPORTING_OTHERS'] = st.slider("Supporting others regularly", 0, 10, 5) | |
| st.markdown("**Health & Lifestyle**") | |
| user_input['FRUITS_VEGGIES'] = st.slider("Fruit & veg servings per day", 0, 10, 5) | |
| user_input['DAILY_STEPS'] = st.slider("Daily steps (thousands)", 0, 10, 5) | |
| user_input['SUFFICIENT_INCOME'] = st.slider("Income sufficiency", 0, 10, 5) | |
| user_input['BMI_RANGE'] = st.slider("BMI range (1=under, 4=obese)", 1, 4, 2) | |
| st.markdown("**Personal Growth**") | |
| user_input['PERSONAL_AWARDS'] = st.slider("Personal awards / recognition", 0, 10, 3) | |
| user_input['DONATION'] = st.slider("Charitable giving", 0, 10, 2) | |
| user_input['PLACES_VISITED'] = st.slider("New places visited recently", 0, 10, 3) | |
| st.markdown("**About you**") | |
| user_input['AGE'] = st.selectbox( | |
| "Age range", [0, 1, 2, 3], | |
| format_func=lambda x: ['Under 20', '21–35', '36–50', '51+'][x] | |
| ) | |
| user_input['GENDER'] = st.selectbox( | |
| "Gender", [0, 1], | |
| format_func=lambda x: ['Female', 'Male'][x] | |
| ) | |
| # Fill in engineered features so the input vector is complete | |
| user_input['RECOVERY_SCORE'] = (user_input['SLEEP_HOURS'] | |
| + user_input['TIME_FOR_PASSION'] | |
| + user_input['WEEKLY_MEDITATION']) | |
| user_input['SOCIAL_SUPPORT_SCORE'] = (user_input['SOCIAL_NETWORK'] | |
| + user_input['CORE_CIRCLE']) | |
| user_input['LIFESTYLE_SCORE'] = (user_input['FLOW'] | |
| + user_input['ACHIEVEMENT'] | |
| + user_input['LIVE_VISION'] | |
| + user_input['TIME_FOR_PASSION']) | |
| user_input['HEALTH_HABITS'] = (user_input['FRUITS_VEGGIES'] | |
| + user_input['SLEEP_HOURS'] | |
| + user_input['TODO_COMPLETED']) | |
| if st.button("🔍 Assess My Burnout Risk", type="primary"): | |
| try: | |
| input_array = np.array([[user_input.get(f, 0) for f in feature_cols]]) | |
| input_scaled = scaler.transform(input_array) | |
| t0 = time.perf_counter() | |
| risk_score = xgb_model.predict_proba(input_scaled)[0][1] | |
| inference_ms = (time.perf_counter() - t0) * 1000 | |
| st.session_state.risk_score = risk_score | |
| st.session_state.inference_time_ms = inference_ms | |
| st.session_state.conversation_history = [] | |
| top_risk_factors = get_top_risk_factors(user_input, feature_cols, xgb_model) | |
| with st.spinner("Getting personalised advice from AI coach..."): | |
| advice = get_burnout_advice(risk_score, top_risk_factors, user_input) | |
| st.session_state.advice = advice | |
| st.session_state.risk_context = ( | |
| f"Risk score: {risk_score:.1%}, " | |
| f"Top factors: {top_risk_factors}" | |
| ) | |
| except Exception as e: | |
| st.error(f"Something went wrong during prediction: {e}") | |
| with col2: | |
| if st.session_state.risk_score is not None: | |
| risk_score = st.session_state.risk_score | |
| st.caption(f"⚡ Model inference: {st.session_state.inference_time_ms:.2f} ms") | |
| st.subheader("Your Burnout Risk") | |
| if risk_score > 0.7: | |
| st.error(f"🚨 High Risk: {risk_score:.1%}") | |
| elif risk_score > 0.4: | |
| st.warning(f"⚠️ Moderate Risk: {risk_score:.1%}") | |
| else: | |
| st.success(f"✅ Low Risk: {risk_score:.1%}") | |
| st.progress(float(risk_score)) | |
| st.subheader("AI Coach Advice") | |
| st.markdown(st.session_state.advice) | |
| st.divider() | |
| st.subheader("💬 Chat with Your AI Coach") | |
| for msg in st.session_state.conversation_history: | |
| role = msg['role'] | |
| st.chat_message(role).write(msg['content']) | |
| if prompt := st.chat_input("Ask your coach anything..."): | |
| st.chat_message("user").write(prompt) | |
| with st.spinner("Thinking..."): | |
| try: | |
| response, st.session_state.conversation_history = get_burnout_chat_response( | |
| st.session_state.conversation_history, | |
| prompt, | |
| st.session_state.risk_context, | |
| ) | |
| except Exception as e: | |
| response = f"Sorry, I couldn't reach the AI coach right now ({e})." | |
| st.chat_message("assistant").write(response) | |
| st.rerun() | |
| if __name__ == '__main__': | |
| main() |